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Hematological changes associated with COVID‐19 infection
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Abstract<sec><title>Background

The unresolved COVID‐19 pandemic considerably impacts the health services in Iraq and worldwide. Consecutive waves of mutated virus increased virus spread and further constrained health systems. Although molecular identification of the virus by polymerase chain reaction is the only recommended method in diagnosing COVID‐19 infection, radiological, biochemical, and hematological studies are substantially important in risk stratification, patient follow‐up, and outcome prediction.

Aim

This narrative review summarized the hematological changes including the blood indices, coagulative indicators, and other associated biochemical laboratory markers in different stages of COVID‐19 infection, highlighting the diagnostic and prognostic significance.

Methods

Literature search was conducted for multiple combinations of different hematological tests and manifestations with novel COVID‐19 using the following key words: “hematological,” “complete blood count,” “lymphopenia,” “blood indices,” “markers” "platelet" OR "thrombocytopenia" AND "COVID‐19," "coronavirus2019," "2019‐nCoV," OR "SARS‐CoV‐2." Articles written in the English language and conducted on human samples between December 2019 and January 2021 were included.

Results

Hematological changes are not reported in asymptomatic or presymptomatic COVID‐19 patients. In nonsevere cases, hematological changes are subtle, included mainly lymphocytopenia (80.4%). In severe, critically ill patients and those with cytokine storm, neutrophilia, lymphocytopenia, elevated D‐dimer, prolonged PT, and reduced fibrinogen are predictors of disease progression and adverse outcome.

Conclusion

Monitoring hematological changes in patients with COVID‐19 can predict patients needing additional care and stratify the risk for severe course of the disease. More studies are required in Iraq to reflect the hematological changes in COVID‐19 as compared to global data.

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Wed Sep 01 2021
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Suggested methods for prediction using semiparametric regression function
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Estimating the Parameters of Exponential-Rayleigh Distribution under Type-I Censored Data
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